TableFeatureGrade.py

import exec

print("\n\n*** DO NOT EXECUTE TWO SCRIPTS ON PARALLEL ***\n\n")

print(" 1) Tier Rank \n 2) Feature Rank")
get_CE = 0 if int(input("Enter choice: ")) == 1 else 1


while(True):
	print(" 1) Adult \n 2) Compas \n 3) Crime \n 4) German \n 5) Health 6) Exit")
	choice = int(input("Enter choice: "))
	data_folder = ""

	if(choice == 1):
		data_folder = "adult"
		reg_params = [1,0.05,0.01] #for linear rbf and poly respectively
		gammas = [0.01,0.03,0.05,0.07,0.09]
		degrees = [3]
		coef0s =  list(range(0,16,3))[1:]
		abstractions = ['raf']
		perturbations = ["top"]
		kernel_types = ['linear','rbf','poly']
		exec.caller(data_folder,reg_params,gammas,degrees,coef0s,abstractions,perturbations,kernel_types,regType = 1,get_avg_bool = False,get_CE = get_CE)
	
	if(choice == 2):
		data_folder = "compas"
		reg_params = [1,0.05,0.01]
		gammas = [0.01,0.03,0.05,0.07,0.09]
		degrees = [3]
		coef0s =  list(range(0,16,3))[1:]
		abstractions = ['raf']
		perturbations = ["top"]
		kernel_types = ['linear','rbf','poly']
		exec.caller(data_folder,reg_params,gammas,degrees,coef0s,abstractions,perturbations,kernel_types,regType = 1,get_avg_bool = False,get_CE = get_CE)
	
	if(choice == 3):
		data_folder = "crime"
		reg_params = [1,1,1]
		gammas = [0.01,0.001,0.0001,0.00001]
		degrees = [3,9,15,20]
		coef0s = [0]
		abstractions = ['raf']
		perturbations = ["top"]
		kernel_types = ['linear','rbf','poly']
		exec.caller(data_folder,reg_params,gammas,degrees,coef0s,abstractions,perturbations,kernel_types,regType = 1,get_avg_bool = False,get_CE = get_CE)
	
	if(choice == 4):
		data_folder = "german"
		reg_params = [1,10,0.01]
		gammas = [0.01,0.03,0.05,0.07,0.09]
		degrees = [6]
		coef0s =  list(range(0,16,3))[1:]
		abstractions = ['raf']
		perturbations = ["top"]
		kernel_types = ['linear','rbf','poly']
		exec.caller(data_folder,reg_params,gammas,degrees,coef0s,abstractions,perturbations,kernel_types,regType = 1,get_avg_bool = False,get_CE = get_CE)
	
	if(choice == 5):
		data_folder = "health"
		reg_params = [0.01,0.1,0.1]
		gammas = [0.01]
		degrees = [3]
		coef0s =  [0.01]
		abstractions = ['raf']
		perturbations = ["top"]
		kernel_types = ['linear','rbf','poly']
		exec.caller(data_folder,reg_params,gammas,degrees,coef0s,abstractions,perturbations,kernel_types,regType = 1,get_avg_bool = False,get_CE = get_CE)

	if(choice == 6):
		break
		
	print(f"\n\n\n OUTPUT LOCATION: \n" +
		f"1) Check ./{data_folder}/{data_folder}-feature_analysis.txt for feature grades" +
	 	"for each SVM. Cummulative feature grades over particular or all kernel at the bottom of the file\n")